Terrorist attacks through building ventilation systems are becoming an increasing concern. In case pollutants are intentionally released in a building with mechanical ventilation systems, it is critical to localize the source and characterize its releasing curve. Previous inverse modeling studies have adopted the adjoint probability method to identify the source location and used the Tikhonov regularization method to determine the source releasing profile, but the selection of the prediction model and determination of the regularization parameter remain challenging. These limitations can affect the identification accuracy and prolong the computational time required. To address the difficulties in solving the inverse problems, this work proposed a Markov-chain-oriented inverse approach to identify the temporal release rate and location of a pollutant source in buildings with ventilation systems and validated it in an experimental chamber. In the modified Markov chain, the source term was discrete by each time step, and the pollutant distribution was directly calculated with no iterations. The forward Markov chain was reversed to characterize the intermittently releasing profile by introducing the Tikhonov regularization method, while the regularized parameter was determined by an automatic iterative discrepancy method. The source location was further estimated by adopting the Bayes inference. With chamber experiments, the effectiveness of the proposed inverse model was validated, and the impact of the sensor performance, quantity and placement, as well as pollutant releasing curves on the identification accuracy of the source intensity was explicitly discussed. Results showed that the inverse model can identify the intermittent releasing rate efficiently and promptly, and the identification error for pollutant releasing curves with complex waveforms is about 20%.

通过建筑物通风系统的恐怖袭击正日益引起人们的关注。如果有意将污染物排放到带有机械通风系统的建筑物中，则对排放源进行定位并确定其释放曲线的特性至关重要。先前的逆建模研究已经采用伴随概率方法来识别源位置，并使用Tikhonov正则化方法来确定源释放轮廓，但是预测模型的选择和正则化参数的确定仍然具有挑战性。这些限制可能会影响识别精度并延长所需的计算时间。为了解决解决逆问题的困难，这项工作提出了一种基于马尔可夫链的逆方法，以识别具有通风系统的建筑物中污染物源的瞬时释放速率和位置，并在实验室内对其进行验证。在改进的马尔可夫链中，源项在每个时间步都是离散的，并且污染物分布是直接计算的，没有迭代。通过引入Tikhonov正则化方法来反转正向马尔可夫链，以表征间歇释放曲线，而正则化参数是通过自动迭代差异方法确定的。通过采用贝叶斯推断进一步估计了源位置。通过腔室实验，验证了所提出逆模型的有效性，以及传感器性能，数量和位置的影响，明确讨论了污染物释放曲线对光源强度识别的准确性。结果表明，该模型能快速有效地识别间歇性释放速率，复杂波形污染物释放曲线的识别误差约为20％。